Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases

Bess, Adam; Berglind, Frej; Mukhopadhyay, Supratik; Bryliński, Michał; Griggs, Nicholas W.; Galliano, Chris

doi:10.1016/j.drudis.2021.10.022

Cited by 18 publications

(6 citation statements)

References 63 publications

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“…Consequently, it is crucial to develop other strategies against SARS-CoV-2 infection, such as antiviral drugs. Through high-throughput screening, drug repurposing, and in silico prediction, many studies have been conducted to identify potential candidates for inhibiting SARS-CoV-2 infection [11] , [12] , [13] , [14] , [15] , [16] . Despite of the potential effects on viral replication and translation, many of these candidates still lack in vitro and in vivo experiments to validate their functions, and the requirement of the long period of clinical trials for human usage [13] , [17] , [18] .…”

Section: Introductionmentioning

confidence: 99%

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

Lin

et al. 2022

Biomedicine & Pharmacotherapy

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Section: Introductionmentioning

confidence: 99%

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

Lin

et al. 2022

Biomedicine & Pharmacotherapy

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“…AI plays a pivotal role in reshaping vaccines and drug development for infectious diseases. For instance, BenevolentAI uses AI to identify potential drug candidates by analyzing vast biomedical datasets (Bess et al, 2022). Atomwise employs AI-driven virtual screening to discover novel small-molecule drugs for infectious diseases (Batool et al, 2019).…”

Section: Ai In the Development Of Vaccines And Drugs For Infectious D...mentioning

confidence: 99%

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Amarachukwu Bernaldine Isiaka,

Vivian Nonyelum Anakwenze,

Chiamaka Rosemary Ilodinso

et al. 2024

ijird

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Infectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.

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“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”

Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning

confidence: 99%

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

Perni

Prokopovich

2022

Sci Rep

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Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.

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Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases

Cited by 18 publications

References 63 publications

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies

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